As shown in our analysis in Section , there is an imbalanced distribution in the book rating data, i.e., certain books are rated very frequently while the majority of items are rated by only a few users. In this section, we investigate to what extent different recommendation algorithms propagate this bias into their recommendations. First, we examine algorithms' overall performance without taking into account how they perform for different users or groups of users based on their tendency towards popular items. In Fig. REF , we illustrate the correlation of book popularity and how often the eight algorithms recommend these books. Among baseline algorithms, we are seeing a strong positive correlation on MostPop showing algorithm tendency to recommend popular items frequently not giving chance to long-tail items and no meaningful correlation on Random as expected. Interestingly, we observe a strong correlation between the popularity of items and their recommendation frequency on NeuMF and BPR with very similar behavior to MostPop. A majority of books were not exposed to users by these algorithms, while popular ones are more frequently highlighted.
Furthermore, our experiment shows a moderate positive correlation in WMF and PF among the Matrix Factorization-based approaches. In contrast to Abdollahpouri et al. [1]} and Kowald et al. [2]} study in the Movie and Music domain, no positive correlation exists in PMF, MF, and NMF, indicating that the latter algorithms in Matrix Factorization-based approaches are not prone to popularity bias in Book-Crossing dataset. Additionally, this suggests that the characteristics of underlying data and the domain could play a key role in determining how recommendation algorithms behave in propagating popularity bias in various domains. Finally, among NN-based state-of-the-art approaches investigated in this study, the VAECF model has a moderately positive correlation and a better performance than the NeuMF model.
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